Modeling the onset advantage in musical instrument recognition.
Autor: | Siedenburg K; Department of Medical Physics and Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky University of Oldenburg, Oldenburg, Germanykai.siedenburg@uni-oldenburg.de, marc.r.schaedler@uni-oldenburg.de, david.huelsmeier@uni-oldenburg.de., Schädler MR; Department of Medical Physics and Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky University of Oldenburg, Oldenburg, Germanykai.siedenburg@uni-oldenburg.de, marc.r.schaedler@uni-oldenburg.de, david.huelsmeier@uni-oldenburg.de., Hülsmeier D; Department of Medical Physics and Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky University of Oldenburg, Oldenburg, Germanykai.siedenburg@uni-oldenburg.de, marc.r.schaedler@uni-oldenburg.de, david.huelsmeier@uni-oldenburg.de. |
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Jazyk: | angličtina |
Zdroj: | The Journal of the Acoustical Society of America [J Acoust Soc Am] 2019 Dec; Vol. 146 (6), pp. EL523. |
DOI: | 10.1121/1.5141369 |
Abstrakt: | Sound onsets provide particularly valuable cues for musical instrument identification by human listeners. It has remained unclear whether this onset advantage is due to enhanced perceptual encoding or the richness of acoustical information during onsets. Here this issue was approached by modeling a recent study on instrument identification from tone excerpts [Siedenburg. (2019). J. Acoust. Soc. Am. 145(2), 1078-1087]. A simple Hidden Markov Model classifier with separable Gabor filterbank features simulated human performance and replicated the onset advantage observed previously for human listeners. These results provide evidence that the onset advantage may be driven by the distinct acoustic qualities of onsets. |
Databáze: | MEDLINE |
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